Development of Pollution Contribution Estimation Algorithms and a Web-Based Automated Program

The contamination of sediments with hazardous substances in the coastal waters of South Korea adversely affects the health of marine ecosystems, causing water quality deterioration, hypoxia, red tides, foul odors, and ecological toxicity. This necessitates rapid identification of pollution sources and their resolution. However, the diversity of pollutant types and sources across regions poses challenges to pollution mitigation and management. To address this, there is a need for technology capable of quantitatively evaluating the contributions of complex pollution sources, as well as a system that supports user-friendly data querying, filtering, and visualization. Such a system should also facilitate information and data sharing among users, fostering collaboration within organizations and with external partners.

In response, we developed an automated program for pollution source attribution, encompassing the entire process from data analysis algorithms to result visualization. The program integrates multiple source attribution methods, including the Chemical Mass Balance (CMB) model, Non-Negative Matrix Factorization (NMF) model, and Bayesian Isotope Mixture (BIM) model, to enhance the identification of pollution origins. To ensure accessibility and ease of use, the program was implemented using R Shiny, a web-based platform built on the R programming language.

The automated program accepts csv files as input to estimate pollution contributions and provides visualization of modeling results through graphs, matrices, and geospatial point contributions on maps. This approach enhances researchers' understanding and facilitates efficient utilization of the results.